LIGHT ADAPTATION AND RELIABILITY IN BLOWFLY PHOTORECEPTORS

1996 ◽  
Vol 07 (04) ◽  
pp. 437-444 ◽  
Author(s):  
R.R. DE RUYTER VAN STEVENINCK ◽  
S.B. LAUGHLIN
Keyword(s):  
Light Adaptation ◽  
Signal To Noise Ratio ◽  
Transduction Efficiency ◽  
Statistical Efficiency ◽  
Signal To Noise ◽  
Low Light ◽  
Frequency Dependent ◽  
Light Levels ◽  
Light Intensities ◽  
Noise Ratio

We characterize the reliability of response of blowfly photoreceptors at different light levels. These cells convey their information by graded potentials. Their reliability is quantified by the frequency-dependent contrast-normalized signal to noise ratio. Independently we estimate the effective photoconversion rate of the cells by counting individual photoconversion events, or quantum bumps, at calibrated low light levels. Comparing both results we quantify the statistical efficiency of photoconversion at higher light intensities, characterizing the transduction efficiency as a function of frequency. The light intensities used in these experiments ranged from about 300 to about 5×105 photoconversions per second per photoreceptor. Over most of this range, statistical efficiencies are within 50% at frequencies up to about 100 Hz.

scholarly journals The Optimal Synapse for Sparse, Binary Signals in the Rod Pathway

2006 ◽  
Vol 18 (1) ◽  
pp. 26-44 ◽  
Author(s):  
Paul T. Clark ◽  
Mark C. W. van Rossum
Keyword(s):  
Transfer Function ◽  
Signal To Noise Ratio ◽  
Bipolar Cells ◽  
Signal To Noise ◽  
Low Light ◽  
Rod Photoreceptors ◽  
Light Levels ◽  
Noise Ratio ◽  
Simulated Images ◽  
Rod Bipolar Cells

The sparsity of photons at very low light levels necessitates a nonlinear synaptic transfer function between the rod photoreceptors and the rod-bipolar cells. We examine different ways to characterize the performance of the pathway: the error rate, two variants of the mutual information, and the signal-to-noise ratio. Simulation of the pathway shows that these approaches yield substantially different performance at very low light levels and that maximizing the signal-to-noise ratio yields the best performance when judged from simulated images. The results are compared to recent data.

Variations in Photoreceptor Response Dynamics Across the Fly Retina

2001 ◽  
Vol 86 (2) ◽  
pp. 950-960 ◽  
Author(s):  
Brian G. Burton ◽  
Ben W. Tatler ◽  
Simon B. Laughlin
Keyword(s):  
Temporal Resolution ◽  
Light Adaptation ◽  
Signal To Noise Ratio ◽  
Calliphora Vicina ◽  
Retinal Neuron ◽  
Response Dynamics ◽  
Signal To Noise ◽  
Spatiotemporal Resolution ◽  
Temporal Properties ◽  
Noise Ratio

Gradients in the spatial properties of retinal cells and their relation to image statistics are well documented. However, less is known of gradients in temporal properties, especially at the level of the photoreceptor for which no account exists. Using light flashes and white-noise-modulated light and current stimuli, we examined the spatial and temporal properties of a single class of photoreceptor (R1–6) within the compound eyes of male blowfly, Calliphora vicina. We find that there is a trend toward higher performance at the front of the eye, both in terms of spatiotemporal resolution and signal-to-noise ratio. The receptive fields of frontal photoreceptors are narrower than those of photoreceptors at the side and back of the eye and response speeds are 20% faster. The signal-to-noise ratio at high frequencies is also greatest at the front of the eye, allowing a 30–40% higher information rate. The power spectra of signals and noise indicate that this elevation of performance results both from shorter responses to individual photons and from a more reliable registration of photon arrival times. These distinctions are characteristic of adaptational changes that normally occur on increasing illumination. However, all photoreceptors were absorbing light at approximately the same mean photon rate during our recordings. We therefore suggest that frontal photoreceptors attain a higher state of light adaptation for a given photon rate. This difference may be achieved by a higher density of (Ca2+ permeable) light-gated channels. Consistent with this hypothesis, membrane-impedance measurements show that frontal photoreceptors have a higher specific conductance than other photoreceptors. This higher conductance provides a better temporal performance but is metabolically expensive. Across the eye, temporal resolution is not proportional to spatial (optical) resolution. Neither is it matched obviously to optic flow. Instead we examine the consequences of an improved temporal resolution in the frontal region for the tracking of small moving targets, a behavior exhibited by male flies. We conclude that the temporal properties of a given class of retinal neuron can vary within a single retina and that this variation may be functionally related to the behavioral requirements of the animal.

Application of frequency-dependent multichannel Wiener filters to detect events in 2D three-component seismometer arrays

Geophysics ◽  
2009 ◽  
Vol 74 (6) ◽  
pp. V133-V141 ◽  
Author(s):  
J. Wang ◽  
F. Tilmann ◽  
R. S. White ◽  
P. Bordoni
Keyword(s):  
Least Squares Method ◽  
Signal To Noise Ratio ◽  
Noise Levels ◽  
Signal To Noise ◽  
Coherent Noise ◽  
Gas Field ◽  
Frequency Dependent ◽  
Wiener Filters ◽  
Noise Ratio

Hydraulic fracture-induced microseismic events in producing oil and gas fields are usually small, and noise levels are high at the surface as a result of the heavy equipment in use. Similarly, in nonhydrocarbon settings, arrays for detecting local earthquakes will benefit from reduced noise levels and the ability to detect smaller events will be increased. We propose a frequency-dependent multichannel Wiener filtering technique with linear constraints that uses an adaptive least-squares method to remove coherent noise in seismic array data. The noise records on several reference channels are used to predict the noise on a primary channel and then can be subtracted from the observed data. On a test with an unconstrained version of this filter, maximal noise suppression leads to signal distortion. Two methods of im-posing constraints then achieve signal preservation. In one case study, synthetic signals are added to noise from a pilot deployment of a hexagonal array (nine three-component seismometers, approximately [Formula: see text]) above a gas field; noise levels are suppressed by up to [Formula: see text] (at [Formula: see text]). In a second case study, natural seismicity recorded at a dense array ([Formula: see text] spacing) in Italy is used, where the application of the filter improves the signal-to-noise ratio (S/N) more than [Formula: see text] (at [Formula: see text]) using 35 stations. In both cases, the performance of the multichannel Wiener filters is significantly better than stacking, espe-cially at lower frequencies where stacking does not help to suppress the coherent noise. The unconstrained version of the filter yields the best improvement in signal-to-noise ratio, but the constrained filter is useful when waveform distortion is unacceptable.

scholarly journals Physical noise propagation in color image construction: a geometrical interpretation

2019 ◽  
Vol 2019 (1) ◽  
pp. 375-380
Author(s):  
Axel Clouet ◽  
Jérôme Vaillant ◽  
David Alleysson
Keyword(s):  
Near Infrared ◽  
Color Image ◽  
Signal To Noise Ratio ◽  
Point Of View ◽  
Signal To Noise ◽  
Low Light ◽  
Image Construction ◽  
Color Sensor ◽  
Light Sensor ◽  
Noise Ratio

To avoid false colors, classical color sensors cut infrared wavelengths for which silicon is sensitive (with the use of an infrared cutoff filter called IR-cut). However, in low light situation, noise can alter images. To increase the amount of photons received by the sensor, in other words, the sensor's sensitivity, it has been proposed to remove the IR-cut for low light applications. In this paper, we analyze if this methodology is beneficial from a signal to noise ratio point of view when the wanted result is a color image. For this aim we recall the formalism behind physical raw image acquisition and color reconstruction. A comparative study is carried out between one classical color sensor and one specific color sensor designed for low light conditions. Simulated results have been computed for both sensors under same exposure settings and show that raw signal to noise ratio is better for the low light sensor. However, its reconstructed color image appears more noisy. Our formalism illustrates geometrically the reasons of this degradation in the case of the low light sensor. It is due on one hand to the higher correlation between spectral channels and on the other hand to the near infrared part of the signal in the raw data which is not intrinsically useful for color.

Noise and light adaptation in rods of the macaque monkey

2000 ◽  
Vol 17 (5) ◽  
pp. 659-666 ◽  
Author(s):  
D.M. SCHNEEWEIS ◽  
J.L. SCHNAPF
Keyword(s):  
Light Adaptation ◽  
Single Photon ◽  
Signal To Noise Ratio ◽  
Macaque Monkey ◽  
Membrane Voltage ◽  
Background Intensity ◽  
Signal To Noise ◽  
Voltage Fluctuations ◽  
Dark Light ◽  
Noise Ratio

Membrane voltage was recorded in rod photoreceptors in retina isolated from macaque monkey. The size of the single photon response and the magnitude of membrane voltage fluctuations were assessed in dark- and light-adapted retina. The “dark light” rate ID, defined as the rate of spontaneous photopigment isomerizations that would produce a variance equivalent to that of the noise measured in the dark, was calculated after matched filtering. The average value of 0.08 s−1 fell at the higher end of psychophysical estimates of dark light in human observers. In light-adapted rods the photon response decreased in amplitude and duration, and the magnitude of the voltage fluctuations increased with increasing background light intensity. The signal-to-noise ratio (SNR) for single rods was defined as the ratio of the peak amplitude of the photon response to the standard deviation of the noise fluctuations. The signal-to-noise ratio for dark-adapted rods SNRD was about 7. With increasing background intensity I, the SNR fell as SNRD(1 + I/ID)−1/2. This function may account for the increment thresholds measured with small brief test flashes in human psychophysical experiments.

Managing frequency‐dependent variations in signal‐to‐noise ratio

2006 ◽  
Author(s):  
Steve McHugo ◽  
Malcolm Francis ◽  
Stephen Pickering ◽  
Alex Cooke WesternGeco
Keyword(s):  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Frequency Dependent ◽  
Noise Ratio
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